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+---
+language: kw
+language_name: Cornish
+language_family: celtic_brythonic
+tags:
+  - wikilangs
+  - nlp
+  - tokenizer
+  - embeddings
+  - n-gram
+  - markov
+  - wikipedia
+  - feature-extraction
+  - sentence-similarity
+  - tokenization
+  - n-grams
+  - markov-chain
+  - text-mining
+  - fasttext
+  - babelvec
+  - vocabulous
+  - vocabulary
+  - monolingual
+  - family-celtic_brythonic
+license: mit
+library_name: wikilangs
+pipeline_tag: text-generation
+datasets:
+  - omarkamali/wikipedia-monthly
+dataset_info:
+  name: wikipedia-monthly
+  description: Monthly snapshots of Wikipedia articles across 300+ languages
+metrics:
+  - name: best_compression_ratio
+    type: compression
+    value: 4.173
+  - name: best_isotropy
+    type: isotropy
+    value: 0.8337
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-10
+---
+
+# Cornish - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Cornish** Wikipedia data.
+We analyze tokenizers, n-gram models, Markov chains, vocabulary statistics, and word embeddings.
+
+## 📋 Repository Contents
+
+### Models & Assets
+
+- Tokenizers (8k, 16k, 32k, 64k)
+- N-gram models (2, 3, 4, 5-gram)
+- Markov chains (context of 1, 2, 3, 4 and 5)
+- Subword N-gram and Markov chains
+- Embeddings in various sizes and dimensions (aligned and unaligned)
+- Language Vocabulary
+- Language Statistics
+
+![Performance Dashboard](visualizations/performance_dashboard.png)
+
+### Analysis and Evaluation
+
+- [1. Tokenizer Evaluation](#1-tokenizer-evaluation)
+- [2. N-gram Model Evaluation](#2-n-gram-model-evaluation)
+- [3. Markov Chain Evaluation](#3-markov-chain-evaluation)
+- [4. Vocabulary Analysis](#4-vocabulary-analysis)
+- [5. Word Embeddings Evaluation](#5-word-embeddings-evaluation)
+- [6. Morphological Analysis (Experimental)](#6--morphological-analysis-experimental)
+- [7. Summary & Recommendations](#7-summary--recommendations)
+- [Metrics Glossary](#appendix-metrics-glossary--interpretation-guide)
+- [Visualizations Index](#visualizations-index)
+
+---
+## 1. Tokenizer Evaluation
+
+![Tokenizer Compression](visualizations/tokenizer_compression.png)
+
+![Tokenizer Fertility](visualizations/tokenizer_fertility.png)
+
+![Tokenizer OOV](visualizations/tokenizer_oov.png)
+
+![Total Tokens](visualizations/tokenizer_total_tokens.png)
+
+### Results
+
+| Vocab Size | Compression | Avg Token Len | UNK Rate | Total Tokens |
+|------------|-------------|---------------|----------|--------------|
+| **8k** | 3.429x | 3.43 | 0.1065% | 186,869 |
+| **16k** | 3.721x | 3.73 | 0.1156% | 172,217 |
+| **32k** | 3.977x | 3.98 | 0.1235% | 161,115 |
+| **64k** | 4.173x 🏆 | 4.18 | 0.1296% | 153,552 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `Arthur Ian Lavender (genys 16 mis Hwevrer yw gwarier sowsnek. bellwolok sowsnek ...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁arthur ▁ian ▁lav ender ▁( genys ▁ 1 6 ▁mis ... (+8 more)` | 18 |
+| 16k | `▁arthur ▁ian ▁lav ender ▁( genys ▁ 1 6 ▁mis ... (+8 more)` | 18 |
+| 32k | `▁arthur ▁ian ▁lav ender ▁( genys ▁ 1 6 ▁mis ... (+8 more)` | 18 |
+| 64k | `▁arthur ▁ian ▁lavender ▁( genys ▁ 1 6 ▁mis ▁hwevrer ... (+7 more)` | 17 |
+
+**Sample 2:** `Christoph Waltz (genys 4 a vis Hedra yn Wien) yw gwarier almaynek hag ostrian. b...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁christ oph ▁walt z ▁( genys ▁ 4 ▁a ▁vis ... (+18 more)` | 28 |
+| 16k | `▁christ oph ▁walt z ▁( genys ▁ 4 ▁a ▁vis ... (+18 more)` | 28 |
+| 32k | `▁christoph ▁waltz ▁( genys ▁ 4 ▁a ▁vis ▁hedra ▁yn ... (+15 more)` | 25 |
+| 64k | `▁christoph ▁waltz ▁( genys ▁ 4 ▁a ▁vis ▁hedra ▁yn ... (+15 more)` | 25 |
+
+**Sample 3:** `Sergei Pavlovich Korolev (12 mis Genver - 14 mis Genver o ynjynor fusen sovietek...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁ser g ei ▁pav l ovich ▁kor ol ev ▁( ... (+16 more)` | 26 |
+| 16k | `▁serg ei ▁pav l ovich ▁kor ol ev ▁( 1 ... (+14 more)` | 24 |
+| 32k | `▁sergei ▁pavl ovich ▁kor ol ev ▁( 1 2 ▁mis ... (+12 more)` | 22 |
+| 64k | `▁sergei ▁pavlovich ▁korolev ▁( 1 2 ▁mis ▁genver ▁- ▁ ... (+9 more)` | 19 |
+
+
+### Key Findings
+
+- **Best Compression:** 64k achieves 4.173x compression
+- **Lowest UNK Rate:** 8k with 0.1065% unknown tokens
+- **Trade-off:** Larger vocabularies improve compression but increase model size
+- **Recommendation:** 32k vocabulary provides optimal balance for production use
+
+---
+## 2. N-gram Model Evaluation
+
+![N-gram Perplexity](visualizations/ngram_perplexity.png)
+
+![N-gram Unique](visualizations/ngram_unique.png)
+
+![N-gram Coverage](visualizations/ngram_coverage.png)
+
+### Results
+
+| N-gram | Variant | Perplexity | Entropy | Unique N-grams | Top-100 Coverage | Top-1000 Coverage |
+|--------|---------|------------|---------|----------------|------------------|-------------------|
+| **2-gram** | Word | 6,140 | 12.58 | 17,327 | 19.9% | 47.0% |
+| **2-gram** | Subword | 280 🏆 | 8.13 | 3,069 | 65.7% | 99.2% |
+| **3-gram** | Word | 8,636 | 13.08 | 20,020 | 16.7% | 39.2% |
+| **3-gram** | Subword | 2,413 | 11.24 | 20,195 | 25.0% | 69.6% |
+| **4-gram** | Word | 12,101 | 13.56 | 28,809 | 15.8% | 36.0% |
+| **4-gram** | Subword | 13,333 | 13.70 | 96,993 | 11.0% | 37.3% |
+| **5-gram** | Word | 7,437 | 12.86 | 18,240 | 18.7% | 42.6% |
+| **5-gram** | Subword | 42,511 | 15.38 | 221,084 | 6.2% | 23.7% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `y n` | 3,849 |
+| 2 | `a n` | 3,256 |
+| 3 | `dhe n` | 2,209 |
+| 4 | `a veu` | 1,834 |
+| 5 | `ev a` | 1,712 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `a dro dhe` | 1,033 |
+| 2 | `yw tre yn` | 711 |
+| 3 | `a wodhya kewsel` | 679 |
+| 4 | `wodhya kewsel kembrek` | 678 |
+| 5 | `km dhiworth loundres` | 677 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `a wodhya kewsel kembrek` | 678 |
+| 2 | `kembra lleoedd canolfan bedwyr` | 676 |
+| 3 | `km dhiworth kardydh ha` | 676 |
+| 4 | `lleoedd canolfan bedwyr yma` | 675 |
+| 5 | `canolfan bedwyr yma hi` | 675 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `kembra lleoedd canolfan bedwyr yma` | 675 |
+| 2 | `lleoedd canolfan bedwyr yma hi` | 675 |
+| 3 | `a wodhya kewsel kembrek pednventydnyow` | 674 |
+| 4 | `braster an poblans yn ha` | 643 |
+| 5 | `o braster an poblans yn` | 638 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `n _` | 116,444 |
+| 2 | `s _` | 97,434 |
+| 3 | `_ a` | 94,959 |
+| 4 | `a _` | 91,201 |
+| 5 | `a n` | 89,956 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `a n _` | 39,084 |
+| 2 | `_ a n` | 33,267 |
+| 3 | `o w _` | 30,057 |
+| 4 | `_ a _` | 27,654 |
+| 5 | `_ h a` | 26,523 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ a n _` | 30,039 |
+| 2 | `_ y n _` | 20,330 |
+| 3 | `a n s _` | 16,203 |
+| 4 | `_ h a _` | 16,012 |
+| 5 | `_ d h e` | 13,152 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ d h e _` | 8,088 |
+| 2 | `s _ a n _` | 5,747 |
+| 3 | `s _ y n _` | 5,446 |
+| 4 | `_ g a n s` | 5,365 |
+| 5 | `g a n s _` | 5,220 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 280
+- **Entropy Trend:** Decreases with larger n-grams (more predictable)
+- **Coverage:** Top-1000 patterns cover ~24% of corpus
+- **Recommendation:** 4-gram or 5-gram for best predictive performance
+
+---
+## 3. Markov Chain Evaluation
+
+![Markov Entropy](visualizations/markov_entropy.png)
+
+![Markov Contexts](visualizations/markov_contexts.png)
+
+![Markov Branching](visualizations/markov_branching.png)
+
+### Results
+
+| Context | Variant | Avg Entropy | Perplexity | Branching Factor | Unique Contexts | Predictability |
+|---------|---------|-------------|------------|------------------|-----------------|----------------|
+| **1** | Word | 0.8579 | 1.812 | 5.27 | 68,677 | 14.2% |
+| **1** | Subword | 0.8370 | 1.786 | 6.02 | 1,609 | 16.3% |
+| **2** | Word | 0.2604 | 1.198 | 1.60 | 359,874 | 74.0% |
+| **2** | Subword | 0.8174 | 1.762 | 4.63 | 9,678 | 18.3% |
+| **3** | Word | 0.0856 | 1.061 | 1.14 | 570,742 | 91.4% |
+| **3** | Subword | 0.7769 | 1.713 | 3.81 | 44,741 | 22.3% |
+| **4** | Word | 0.0299 🏆 | 1.021 | 1.05 | 648,256 | 97.0% |
+| **4** | Subword | 0.6461 | 1.565 | 2.69 | 170,507 | 35.4% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `a lettyas nebes is ha tornyaseth yw šiprage map devy buhez mab nechtan cenél ngabráin dre`
+2. `an poblans an brassa niver a dro dhe rutheniom niver a wra medhogyon heb fugieth amerikanek`
+3. `yn asi yn afrika keskreunys a wra an ordinalia ha radn a melbost o 6 mis`
+
+**Context Size 2:**
+
+1. `y n seson segh hir hirder an kensa 10 perfydh besketh en istori amerika ̺ kansvledhen a`
+2. `a n omsav kregys veu parson korlan wosa omsav kethyon afrikan erbynn aga mesters frynkek an wlas`
+3. `dhe n golanes ev ew broder cy davyth fear skrifednyas an orsedh dyllys gans pab leo x`
+
+**Context Size 3:**
+
+1. `a dro dhe vewnans teylu rag ensampel demedhi a ji dhe n goos ankebmyn ew dhe n virus`
+2. `yw tre yn sir ddinbych kembra lleoedd canolfan bedwyr yma hi 47 9 mildir 77 km dhiworth kardydh`
+3. `a wodhya kewsel kembrek pednventydnyow yn kembra kembra`
+
+**Context Size 4:**
+
+1. `a wodhya kewsel kembrek pednventydnyow yn kembra kembra`
+2. `km dhiworth kardydh ha 150 7 m 242 6 km dhiworth loundres 235 o braster an poblans yn ha`
+3. `kembra lleoedd canolfan bedwyr yma hi 47 3 mildir 76 1 km dhiworth kardydh ha 153 8 m 247`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `_owa_aglkedhabur`
+2. `erdyn_nten)_s_do`
+3. `aiem_ow,_y_46_au`
+
+**Context Size 2:**
+
+1. `n_miskriusys_ra_e`
+2. `s_ani_hballs_gans`
+3. `_ascrott_en:_που,`
+
+**Context Size 3:**
+
+1. `an_a_bys_o_an_sewy`
+2. `_an_mygydnyow_dory`
+3. `ow_boosdhe_dhe_dhe`
+
+**Context Size 4:**
+
+1. `_an_dowr_e'n_esel_s`
+2. `_yn_kodhasow_bygh_1`
+3. `ans_doemm_an_rebel.`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 97.0% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (170,507 contexts)
+- **Recommendation:** Context-3 or Context-4 for text generation
+
+---
+## 4. Vocabulary Analysis
+
+![Zipf's Law](visualizations/zipf_law.png)
+
+![Top Words](visualizations/top20_words.png)
+
+![Coverage Curve](visualizations/vocab_coverage.png)
+
+### Statistics
+
+| Metric | Value |
+|--------|-------|
+| Vocabulary Size | 30,471 |
+| Total Tokens | 725,474 |
+| Mean Frequency | 23.81 |
+| Median Frequency | 4 |
+| Frequency Std Dev | 361.46 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | a | 35,840 |
+| 2 | an | 30,880 |
+| 3 | yn | 21,945 |
+| 4 | ha | 18,075 |
+| 5 | n | 12,791 |
+| 6 | yw | 12,421 |
+| 7 | dhe | 10,462 |
+| 8 | y | 10,232 |
+| 9 | o | 6,009 |
+| 10 | gans | 5,241 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | tinethy | 2 |
+| 2 | chislehurst | 2 |
+| 3 | pensions | 2 |
+| 4 | gluthys | 2 |
+| 5 | recayt | 2 |
+| 6 | aunt | 2 |
+| 7 | lyasow | 2 |
+| 8 | calabresi | 2 |
+| 9 | prinsipya | 2 |
+| 10 | romanzo | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 1.0615 |
+| R² (Goodness of Fit) | 0.995825 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 41.6% |
+| Top 1,000 | 67.7% |
+| Top 5,000 | 85.0% |
+| Top 10,000 | 91.5% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9958 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 41.6% of corpus
+- **Long Tail:** 20,471 words needed for remaining 8.5% coverage
+
+---
+## 5. Word Embeddings Evaluation
+
+![Embedding Isotropy](visualizations/embedding_isotropy.png)
+
+![Similarity Matrix](visualizations/embedding_similarity.png)
+
+![t-SNE Words](visualizations/tsne_words.png)
+
+![t-SNE Sentences](visualizations/tsne_sentences.png)
+
+
+### 5.1 Cross-Lingual Alignment
+
+![Alignment Quality](visualizations/embedding_alignment_quality.png)
+
+![Multilingual t-SNE](visualizations/embedding_tsne_multilingual.png)
+
+
+### 5.2 Model Comparison
+
+| Model | Dimension | Isotropy | Semantic Density | Alignment R@1 | Alignment R@10 |
+|-------|-----------|----------|------------------|---------------|----------------|
+| **mono_32d** | 32 | 0.8337 | 0.3251 | N/A | N/A |
+| **mono_64d** | 64 | 0.5460 | 0.2971 | N/A | N/A |
+| **mono_128d** | 128 | 0.1358 | 0.2890 | N/A | N/A |
+| **aligned_32d** | 32 | 0.8337 🏆 | 0.3307 | 0.0380 | 0.2340 |
+| **aligned_64d** | 64 | 0.5460 | 0.2936 | 0.0580 | 0.2660 |
+| **aligned_128d** | 128 | 0.1358 | 0.2812 | 0.0940 | 0.3220 |
+
+### Key Findings
+
+- **Best Isotropy:** aligned_32d with 0.8337 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.3028. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 9.4% R@1 in cross-lingual retrieval.
+- **Recommendation:** 128d aligned for best cross-lingual performance
+
+---
+## 6.  Morphological Analysis (Experimental)
+
+This section presents an automated morphological analysis derived from the statistical divergence between word-level and subword-level models. By analyzing where subword predictability spikes and where word-level coverage fails, we can infer linguistic structures without supervised data.
+
+### 6.1 Productivity & Complexity
+
+| Metric | Value | Interpretation | Recommendation |
+|--------|-------|----------------|----------------|
+| Productivity Index | **5.000** | High morphological productivity | Reliable analysis |
+| Idiomaticity Gap | **0.802** | High formulaic/idiomatic content | - |
+
+### 6.2 Affix Inventory (Productive Units)
+
+These are the most productive prefixes and suffixes identified by sampling the vocabulary for global substitutability patterns. A unit is considered an affix if stripping it leaves a valid stem that appears in other contexts.
+
+#### Productive Prefixes
+| Prefix | Examples |
+|--------|----------|
+| `-s` | sufi, sempelhes, surhe |
+| `-d` | dolly, doeg, diskargans |
+| `-a` | andy, amstyryus, aghskrifer |
+| `-g` | gwiska, group, gwedhek |
+| `-b` | bual, baronetage, barjavel |
+| `-k` | kuršių, krestennogyon, keshevelyans |
+| `-p` | peblys, provyans, pygmaea |
+| `-t` | trohag, troha, tyghtya |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-s` | peblys, iseldiryekdedhyas, norvys |
+| `-n` | chinkapin, elfyn, krestennogyon |
+| `-ow` | megyansow, filmow, posow |
+| `-w` | megyansow, filmow, wiw |
+| `-a` | gwiska, bianna, wosa |
+| `-k` | unnek, gwedhek, vywoniethek |
+| `-on` | krestennogyon, menystroryon, kwarton |
+| `-h` | babergh, bouddydh, priweyth |
+
+### 6.3 Bound Stems (Lexical Roots)
+
+Bound stems are high-frequency subword units that are semantically cohesive but rarely appear as standalone words. These often correspond to the 'core' of a word that requires inflection or derivation to be valid.
+
+| Stem | Cohesion | Substitutability | Examples |
+|------|----------|------------------|----------|
+| `skri` | 1.99x | 54 contexts | skrif, skrij, skrin |
+| `yans` | 1.73x | 71 contexts | usyans, unyans, wayans |
+| `krif` | 1.92x | 27 contexts | skrif, skrift, skrifa |
+| `eyth` | 1.53x | 57 contexts | neyth, leyth, seyth |
+| `anso` | 2.04x | 20 contexts | ganso, kansow, sansom |
+| `edhy` | 1.53x | 54 contexts | hedhys, dedhya, anedhy |
+| `nnow` | 2.01x | 20 contexts | lynnow, donnow, vonnow |
+| `nsow` | 2.05x | 18 contexts | vynsow, kansow, ponsow |
+| `ened` | 1.92x | 17 contexts | wened, senedd, venedh |
+| `edhe` | 1.37x | 52 contexts | edhen, hedhew, wedhen |
+| `lans` | 1.65x | 26 contexts | plans, blans, kalans |
+| `dhya` | 1.53x | 32 contexts | dedhya, tydhya, tedhya |
+
+### 6.4 Affix Compatibility (Co-occurrence)
+
+This table shows which prefixes and suffixes most frequently co-occur on the same stems, revealing the 'stacking' rules of the language's morphology.
+
+| Prefix | Suffix | Frequency | Examples |
+|--------|--------|-----------|----------|
+| `-d` | `-s` | 189 words | definys, dielvednans |
+| `-g` | `-s` | 98 words | gevres, glaucoides |
+| `-k` | `-s` | 90 words | kows, kerwys |
+| `-k` | `-w` | 80 words | krow, kalenderyow |
+| `-p` | `-s` | 79 words | pleasants, porpos |
+| `-k` | `-ow` | 78 words | krow, kalenderyow |
+| `-d` | `-ns` | 75 words | dielvednans, dhielvennans |
+| `-a` | `-s` | 73 words | antarcticus, arvreusyas |
+| `-s` | `-s` | 70 words | skwattys, shackys |
+| `-t` | `-s` | 69 words | tredhinas, trehevis |
+
+### 6.5 Recursive Morpheme Segmentation
+
+Using **Recursive Hierarchical Substitutability**, we decompose complex words into their constituent morphemes. This approach handles nested affixes (e.g., `prefix-prefix-root-suffix`).
+
+| Word | Suggested Split | Confidence | Stem |
+|------|-----------------|------------|------|
+| politikel | **`politi-k-el`** | 7.5 | `k` |
+| lanndreth | **`lannd-re-th`** | 7.5 | `re` |
+| degvledhen | **`de-g-vledhen`** | 7.5 | `vledhen` |
+| anserhogath | **`anserhog-a-th`** | 7.5 | `a` |
+| harryhausen | **`harryhau-s-en`** | 7.5 | `s` |
+| haakonsson | **`haakons-s-on`** | 7.5 | `s` |
+| klavjiores | **`klavjio-r-es`** | 7.5 | `r` |
+| daskorrys | **`da-skorr-ys`** | 6.0 | `skorr` |
+| sewyansow | **`sewya-ns-ow`** | 6.0 | `sewya` |
+| fondyansow | **`fondya-ns-ow`** | 6.0 | `fondya` |
+| tetroksid | **`te-tr-oksid`** | 6.0 | `oksid` |
+| wordhonek | **`wordh-on-ek`** | 6.0 | `wordh` |
+| gonisogethel | **`gonisogeth-el`** | 4.5 | `gonisogeth` |
+| delinyans | **`delinya-ns`** | 4.5 | `delinya` |
+| guntellas | **`guntella-s`** | 4.5 | `guntella` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Cornish shows high morphological productivity. The subword models are significantly more efficient than word models, suggesting a rich system of affixation or compounding.
+
+> **Note on Idiomaticity:** The high Idiomaticity Gap suggests a large number of frequent multi-word expressions or formulaic sequences that are statistically distinct from their component parts.
+
+---
+## 7. Summary & Recommendations
+
+![Performance Dashboard](visualizations/performance_dashboard.png)
+
+### Production Recommendations
+
+| Component | Recommended | Rationale |
+|-----------|-------------|-----------|
+| Tokenizer | **64k BPE** | Best compression (4.17x) |
+| N-gram | **2-gram** | Lowest perplexity (280) |
+| Markov | **Context-4** | Highest predictability (97.0%) |
+| Embeddings | **100d** | Balanced semantic capture and isotropy |
+
+
+---
+## Appendix: Metrics Glossary & Interpretation Guide
+
+This section provides definitions, intuitions, and guidance for interpreting the metrics used throughout this report.
+
+### Tokenizer Metrics
+
+**Compression Ratio**
+> *Definition:* The ratio of characters to tokens (chars/token). Measures how efficiently the tokenizer represents text.
+>
+> *Intuition:* Higher compression means fewer tokens needed to represent the same text, reducing sequence lengths for downstream models. A 3x compression means ~3 characters per token on average.
+>
+> *What to seek:* Higher is generally better for efficiency, but extremely high compression may indicate overly aggressive merging that loses morphological information.
+
+**Average Token Length (Fertility)**
+> *Definition:* Mean number of characters per token produced by the tokenizer.
+>
+> *Intuition:* Reflects the granularity of tokenization. Longer tokens capture more context but may struggle with rare words; shorter tokens are more flexible but increase sequence length.
+>
+> *What to seek:* Balance between 2-5 characters for most languages. Arabic/morphologically-rich languages may benefit from slightly longer tokens.
+
+**Unknown Token Rate (OOV Rate)**
+> *Definition:* Percentage of tokens that map to the unknown/UNK token, indicating words the tokenizer cannot represent.
+>
+> *Intuition:* Lower OOV means better vocabulary coverage. High OOV indicates the tokenizer encounters many unseen character sequences.
+>
+> *What to seek:* Below 1% is excellent; below 5% is acceptable. BPE tokenizers typically achieve very low OOV due to subword fallback.
+
+### N-gram Model Metrics
+
+**Perplexity**
+> *Definition:* Measures how "surprised" the model is by test data. Mathematically: 2^(cross-entropy). Lower values indicate better prediction.
+>
+> *Intuition:* If perplexity is 100, the model is as uncertain as if choosing uniformly among 100 options at each step. A perplexity of 10 means effectively choosing among 10 equally likely options.
+>
+> *What to seek:* Lower is better. Perplexity decreases with larger n-grams (more context). Values vary widely by language and corpus size.
+
+**Entropy**
+> *Definition:* Average information content (in bits) needed to encode the next token given the context. Related to perplexity: perplexity = 2^entropy.
+>
+> *Intuition:* High entropy means high uncertainty/randomness; low entropy means predictable patterns. Natural language typically has entropy between 1-4 bits per character.
+>
+> *What to seek:* Lower entropy indicates more predictable text patterns. Entropy should decrease as n-gram size increases.
+
+**Coverage (Top-K)**
+> *Definition:* Percentage of corpus occurrences explained by the top K most frequent n-grams.
+>
+> *Intuition:* High coverage with few patterns indicates repetitive/formulaic text; low coverage suggests diverse vocabulary usage.
+>
+> *What to seek:* Depends on use case. For language modeling, moderate coverage (40-60% with top-1000) is typical for natural text.
+
+### Markov Chain Metrics
+
+**Average Entropy**
+> *Definition:* Mean entropy across all contexts, measuring average uncertainty in next-word prediction.
+>
+> *Intuition:* Lower entropy means the model is more confident about what comes next. Context-1 has high entropy (many possible next words); Context-4 has low entropy (few likely continuations).
+>
+> *What to seek:* Decreasing entropy with larger context sizes. Very low entropy (<0.1) indicates highly deterministic transitions.
+
+**Branching Factor**
+> *Definition:* Average number of unique next tokens observed for each context.
+>
+> *Intuition:* High branching = many possible continuations (flexible but uncertain); low branching = few options (predictable but potentially repetitive).
+>
+> *What to seek:* Branching factor should decrease with context size. Values near 1.0 indicate nearly deterministic chains.
+
+**Predictability**
+> *Definition:* Derived metric: (1 - normalized_entropy) × 100%. Indicates how deterministic the model's predictions are.
+>
+> *Intuition:* 100% predictability means the next word is always certain; 0% means completely random. Real text falls between these extremes.
+>
+> *What to seek:* Higher predictability for text generation quality, but too high (>98%) may produce repetitive output.
+
+### Vocabulary & Zipf's Law Metrics
+
+**Zipf's Coefficient**
+> *Definition:* The slope of the log-log plot of word frequency vs. rank. Zipf's law predicts this should be approximately -1.
+>
+> *Intuition:* A coefficient near -1 indicates the corpus follows natural language patterns where a few words are very common and most words are rare.
+>
+> *What to seek:* Values between -0.8 and -1.2 indicate healthy natural language distribution. Deviations may suggest domain-specific or artificial text.
+
+**R² (Coefficient of Determination)**
+> *Definition:* Measures how well the linear fit explains the frequency-rank relationship. Ranges from 0 to 1.
+>
+> *Intuition:* R² near 1.0 means the data closely follows Zipf's law; lower values indicate deviation from expected word frequency patterns.
+>
+> *What to seek:* R² > 0.95 is excellent; > 0.99 indicates near-perfect Zipf adherence typical of large natural corpora.
+
+**Vocabulary Coverage**
+> *Definition:* Cumulative percentage of corpus tokens accounted for by the top N words.
+>
+> *Intuition:* Shows how concentrated word usage is. If top-100 words cover 50% of text, the corpus relies heavily on common words.
+>
+> *What to seek:* Top-100 covering 30-50% is typical. Higher coverage indicates more repetitive text; lower suggests richer vocabulary.
+
+### Word Embedding Metrics
+
+**Isotropy**
+> *Definition:* Measures how uniformly distributed vectors are in the embedding space. Computed as the ratio of minimum to maximum singular values.
+>
+> *Intuition:* High isotropy (near 1.0) means vectors spread evenly in all directions; low isotropy means vectors cluster in certain directions, reducing expressiveness.
+>
+> *What to seek:* Higher isotropy generally indicates better-quality embeddings. Values > 0.1 are reasonable; > 0.3 is good. Lower-dimensional embeddings tend to have higher isotropy.
+
+**Average Norm**
+> *Definition:* Mean magnitude (L2 norm) of word vectors in the embedding space.
+>
+> *Intuition:* Indicates the typical "length" of vectors. Consistent norms suggest stable training; high variance may indicate some words are undertrained.
+>
+> *What to seek:* Relatively consistent norms across models. The absolute value matters less than consistency (low std deviation).
+
+**Cosine Similarity**
+> *Definition:* Measures angular similarity between vectors, ranging from -1 (opposite) to 1 (identical direction).
+>
+> *Intuition:* Words with similar meanings should have high cosine similarity. This is the standard metric for semantic relatedness in embeddings.
+>
+> *What to seek:* Semantically related words should score > 0.5; unrelated words should be near 0. Synonyms often score > 0.7.
+
+**t-SNE Visualization**
+> *Definition:* t-Distributed Stochastic Neighbor Embedding - a dimensionality reduction technique that preserves local structure for visualization.
+>
+> *Intuition:* Clusters in t-SNE plots indicate groups of semantically related words. Spread indicates vocabulary diversity; tight clusters suggest semantic coherence.
+>
+> *What to seek:* Meaningful clusters (e.g., numbers together, verbs together). Avoid over-interpreting distances - t-SNE preserves local, not global, structure.
+
+### General Interpretation Guidelines
+
+1. **Compare within model families:** Metrics are most meaningful when comparing models of the same type (e.g., 8k vs 64k tokenizer).
+2. **Consider trade-offs:** Better performance on one metric often comes at the cost of another (e.g., compression vs. OOV rate).
+3. **Context matters:** Optimal values depend on downstream tasks. Text generation may prioritize different metrics than classification.
+4. **Corpus influence:** All metrics are influenced by corpus characteristics. Wikipedia text differs from social media or literature.
+5. **Language-specific patterns:** Morphologically rich languages (like Arabic) may show different optimal ranges than analytic languages.
+
+
+### Visualizations Index
+
+| Visualization | Description |
+|---------------|-------------|
+| Tokenizer Compression | Compression ratios by vocabulary size |
+| Tokenizer Fertility | Average token length by vocabulary |
+| Tokenizer OOV | Unknown token rates |
+| Tokenizer Total Tokens | Total tokens by vocabulary |
+| N-gram Perplexity | Perplexity by n-gram size |
+| N-gram Entropy | Entropy by n-gram size |
+| N-gram Coverage | Top pattern coverage |
+| N-gram Unique | Unique n-gram counts |
+| Markov Entropy | Entropy by context size |
+| Markov Branching | Branching factor by context |
+| Markov Contexts | Unique context counts |
+| Zipf's Law | Frequency-rank distribution with fit |
+| Vocab Frequency | Word frequency distribution |
+| Top 20 Words | Most frequent words |
+| Vocab Coverage | Cumulative coverage curve |
+| Embedding Isotropy | Vector space uniformity |
+| Embedding Norms | Vector magnitude distribution |
+| Embedding Similarity | Word similarity heatmap |
+| Nearest Neighbors | Similar words for key terms |
+| t-SNE Words | 2D word embedding visualization |
+| t-SNE Sentences | 2D sentence embedding visualization |
+| Position Encoding | Encoding method comparison |
+| Model Sizes | Storage requirements |
+| Performance Dashboard | Comprehensive performance overview |
+
+---
+## About This Project
+
+### Data Source
+
+Models trained on [wikipedia-monthly](https://huggingface.co/datasets/omarkamali/wikipedia-monthly) - a monthly snapshot of Wikipedia articles across 300+ languages.
+
+### Project
+
+A project by **[Wikilangs](https://wikilangs.org)** - Open-source NLP models for every Wikipedia language.
+
+### Maintainer
+
+[Omar Kamali](https://omarkamali.com) - [Omneity Labs](https://omneitylabs.com)
+
+### Citation
+
+If you use these models in your research, please cite:
+
+```bibtex
+@misc{wikilangs2025,
+  author = {Kamali, Omar},
+  title = {Wikilangs: Open NLP Models for Wikipedia Languages},
+  year = {2025},
+  doi = {10.5281/zenodo.18073153},
+  publisher = {Zenodo},
+  url = {https://huggingface.co/wikilangs}
+  institution = {Omneity Labs}
+}
+```
+
+### License
+
+MIT License - Free for academic and commercial use.
+
+### Links
+
+- 🌐 Website: [wikilangs.org](https://wikilangs.org)
+- 🤗 Models: [huggingface.co/wikilangs](https://huggingface.co/wikilangs)
+- 📊 Data: [wikipedia-monthly](https://huggingface.co/datasets/omarkamali/wikipedia-monthly)
+- 👤 Author: [Omar Kamali](https://huggingface.co/omarkamali)
+- 🤝 Sponsor: [Featherless AI](https://featherless.ai)
+---
+*Generated by Wikilangs Models Pipeline*
+
+*Report Date: 2026-01-10 08:58:14*
diff --git a/kw_morph_tokenizer.json b/kw_morph_tokenizer.json
new file mode 100644
index 0000000000000000000000000000000000000000..7f2d95a6ab0d4b7bd2e129facaf59603f8a345c8
--- /dev/null
+++ b/kw_morph_tokenizer.json
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diff --git a/models/tokenizer/kw_tokenizer_64k.model b/models/tokenizer/kw_tokenizer_64k.model
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diff --git a/models/tokenizer/kw_tokenizer_64k.vocab b/models/tokenizer/kw_tokenizer_64k.vocab
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+▁winslow	-39157
+▁winters	-39158
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+▁woolaes	-39162
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+▁wrexham	-39168
+▁writers	-39169
+▁wusigen	-39170
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+▁zaedyus	-39182
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+▁zuberoa	-39188
+▁éadmund	-39189
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+▁ελληνικ	-39196
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+▁квартет	-39202
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+tusvultur	-39310
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+ywodraeth	-39320
+▁aardvark	-39321
+▁abdullah	-39322
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+▁abertawa	-39324
+▁abingdon	-39325
+▁absolute	-39326
+▁académie	-39327
+▁accessed	-39328
+▁accomack	-39329
+▁activist	-39330
+▁adhyskas	-39331
+▁adhysker	-39332
+▁adhyskes	-39333
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+▁aestivum	-39335
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+▁aghskrif	-39337
+▁agwedhow	-39338
+▁aitvarai	-39339
+▁akontydh	-39340
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+▁albanyon	-39346
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+▁aloysius	-39351
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+▁amazonas	-39356
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+▁ammethel	-39358
+▁ammethys	-39359
+▁amphibia	-39360
+▁anallans	-39361
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+▁anatolek	-39363
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+▁anerobek	-39365
+▁anfaster	-39366
+▁anfeusik	-39367
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+▁anglesey	-39369
+▁angwilla	-39370
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+▁ankemmyn	-39372
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+▁antiqwar	-39377
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+▁anvarwel	-39379
+▁aorelian	-39380
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+▁arbednig	-39383
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+▁aswonnyn	-39407
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+▁auckland	-39410
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+▁balladys	-39421
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+▁basaltow	-39423
+▁basileía	-39424
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+▁bedhygla	-39426
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+▁bewonydh	-39440
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+▁biwmares	-39443
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+▁blonegow	-39445
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+▁boghwynn	-39447
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+▁calamita	-39481
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+▁carousel	-39487
+▁casowari	-39488
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+▁catarina	-39490
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+▁chashtal	-39494
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+▁chomutov	-39497
+▁chulainn	-39498
+▁chuzhaya	-39499
+▁chwedlau	-39500
+▁chwedleu	-39501
+▁circinus	-39502
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+▁commedia	-39510
+▁commerce	-39511
+▁composys	-39512
+▁computer	-39513
+▁concours	-39514
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+▁connelly	-39516
+▁corvidae	-39517
+▁cotopaxi	-39518
+▁courtney	-39519
+▁coyoacán	-39520
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+▁cromwell	-39525
+▁crosland	-39526
+▁cuailgne	-39527
+▁cultivar	-39528
+▁cunliffe	-39529
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+▁dhalghen	-39559
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+▁dhiyskyn	-39580
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+▁dialects	-39588
+▁diatomow	-39589
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+▁dibennys	-39591
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+▁difresek	-39594
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+▁difreudh	-39596
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+▁dinoflag	-39598
+▁dinorwig	-39599
+▁dinosaur	-39600
+▁diplomen	-39601
+▁disillus	-39602
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+▁distance	-39605
+▁distowgh	-39606
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+▁divroysi	-39608
+▁diwelven	-39609
+▁diwvregh	-39610
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+▁dohajydh	-39612
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+▁ekologel	-39639
+▁eksamnya	-39640
+▁eksokorf	-39641
+▁elektrek	-39642
+▁elmensis	-39643
+▁emotions	-39644
+▁eniwetok	-39645
+▁enperthi	-39646
+▁ensemble	-39647
+▁epilepsi	-39648
+▁equation	-39649
+▁equuleus	-39650
+▁eridanus	-39651
+▁eriksson	-39652
+▁erlyvrow	-39653
+▁esethvos	-39654
+▁eskeryow	-39655
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+▁eskorrva	-39658
+▁eskravus	-39659
+▁estemyes	-39660
+▁estewlys	-39661
+▁etruscus	-39662
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+▁eulogium	-39664
+▁evellyon	-39665
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+▁eyljunya	-39667
+▁eylworra	-39668
+▁falladow	-39669
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+▁faragher	-39671
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+▁fatimids	-39673
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+▁feabhail	-39676
+▁february	-39677
+▁fennsita	-39678
+▁fernando	-39679
+▁fethyans	-39680
+▁feuillet	-39681
+▁fightyon	-39682
+▁filosofi	-39683
+▁finsbury	-39684
+▁finwetha	-39685
+▁flaherty	-39686
+▁flamengo	-39687
+▁flanders	-39688
+▁flandrys	-39689
+▁fletcher	-39690
+▁fluvanna	-39691
+▁fogowyow	-39692
+▁formally	-39693
+▁fosfolip	-39694
+▁fountain	-39695
+▁fracture	-39696
+▁fragilis	-39697
+▁franketh	-39698
+▁frankiom	-39699
+▁frankton	-39700
+▁frankyon	-39701
+▁français	-39702
+▁frederic	-39703
+▁frederik	-39704
+▁freiherr	-39705
+▁frideric	-39706
+▁frontier	-39707
+▁frédéric	-39708
+▁fusarium	-39709
+▁fusednow	-39710
+▁fylystyn	-39711
+▁gaillimh	-39712
+▁galosegi	-39713
+▁gamaliel	-39714
+▁gampolla	-39715
+▁gannasow	-39716
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+▁garbonat	-39718
+▁garoryon	-39719
+▁gascoyne	-39720
+▁gelyonen	-39721
+▁genderow	-39722
+▁genydnek	-39723
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+▁germaine	-39726
+▁germocus	-39727
+▁gertrude	-39728
+▁geskanow	-39729
+▁gesoberi	-39730
+▁gevrenna	-39731
+▁gidlyver	-39732
+▁ginzburg	-39733
+▁glaswydn	-39734
+▁glendale	-39735
+▁glenties	-39736
+▁gniviles	-39737
+▁goderrys	-39738
+▁godrosow	-39739
+▁godzilla	-39740
+▁goesheyl	-39741
+▁gogellek	-39742
+▁gogrysys	-39743
+▁golowyow	-39744
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+▁gomendys	-39746
+▁gompanis	-39747
+▁gonisyas	-39748
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+▁goongrug	-39750
+▁gormelys	-39751
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+▁gorskeus	-39753
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+▁gorwedha	-39755
+▁gospodin	-39756
+▁gostoedh	-39757
+▁goswigow	-39758
+▁gotheneb	-39759
+▁gothmans	-39760
+▁gottlieb	-39761
+▁governya	-39762
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+▁gwageles	-39771
+▁gwandras	-39772
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+▁gweneseg	-39780
+▁gwengamp	-39781
+▁gwenogus	-39782
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+▁gwredhek	-39791
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+▁hedeudhi	-39804
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+▁henderow	-39810
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+▁hispania	-39820
+▁holanedh	-39821
+▁holbrook	-39822
+▁homparya	-39823
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+▁prolactin	-41160
+▁prolaktin	-41161
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+▁pyrolusit	-41166
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+▁randyryow	-41172
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+▁rhonwellt	-41188
+▁rigoberta	-41189
+▁rockville	-41190
+▁rodriguez	-41191
+▁romansydh	-41192
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+▁rostormel	-41194
+▁rotavirus	-41195
+▁rubiaceae	-41196
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+▁samogitek	-41199
+▁samoyedek	-41200
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+▁sterennek	-41236
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+▁theleskop	-41265
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+▁vethydnow	-41321
+▁vethynnow	-41322
+▁vewnansow	-41323
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+▁ymerodrol	-41361
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+▁zionydhek	-41367
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+▁обитатели	-41372
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+▁alkaloydow	-41408
+▁allegretti	-41409
+▁almaynstat	-41410
+▁anatomieth	-41411
+▁andhiblans	-41412
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+▁anpuredhow	-41415
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+▁anvateryel	-41418
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+▁arghepskop	-41421
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+▁arnowyttha	-41424
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+▁battleship	-41437
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+▁boragweyth	-41456
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+▁brithweyth	-41463
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+▁carangidae	-41469
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+▁chancellor	-41479
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+▁folkestone	-41600
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+▁konsloryon	-41724
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+▁kowethajek	-41728
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+▁krogrewliv	-41735
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+▁krysiethek	-41737
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+▁kuomintang	-41739
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+▁kymygennow	-41743
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+▁königsberg	-41745
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+▁lawrenciom	-41753
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+▁lettryseth	-41755
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+▁llandrillo	-41759
+▁llanelwedd	-41760
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+▁llanilltud	-41762
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+▁lowarthyow	-41764
+▁lurkyoryon	-41765
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+▁mahershala	-41768
+▁makedoniek	-41769
+▁mankeynyon	-41770
+▁marthusyon	-41771
+▁mathematek	-41772
+▁mcguinness	-41773
+▁medhygneth	-41774
+▁mediterran	-41775
+▁meillionen	-41776
+▁meitneriom	-41777
+▁meksikanek	-41778
+▁melicoccus	-41779
+▁melwhednow	-41780
+▁meningitis	-41781
+▁menowghter	-41782
+▁mentenyans	-41783
+▁middletown	-41784
+▁milimetrow	-41785
+▁misérables	-41786
+▁mockingjay	-41787
+▁moldoveanu	-41788
+▁molybdenit	-41789
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+▁ratatouille	-42321
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+▁astronomical	-42408
+▁astudiaethau	-42409
+▁athbaskannek	-42410
+▁azerbaijanek	-42411
+▁azerbayjanek	-42412
+▁bersekutyans	-42413
+▁bewliesekter	-42414
+▁bleujyowadow	-42415
+▁bolitegoryon	-42416
+▁bortyngalysi	-42417
+▁bradypodidae	-42418
+▁brasiliensis	-42419
+▁breydhyoryon	-42420
+▁burjestrevow	-42421
+▁byghanlesser	-42422
+▁callisthenes	-42423
+▁casterbridge	-42424
+▁chambersburg	-42425
+▁chilechilean	-42426
+▁chlamyphorus	-42427
+▁cirrocumulus	-42428
+▁comprehendys	-42429
+▁condorcondor	-42430
+▁connochaetes	-42431
+▁conquistador	-42432
+▁conservation	-42433
+▁conservative	-42434
+▁crocodylidae	-42435
+▁cyclopedidae	-42436
+▁cyfesurynnau	-42437
+▁cynocephalus	-42438
+▁danbellednow	-42439
+▁darsewyansow	-42440
+▁dastallethys	-42441
+▁deknologieth	-42442
+▁desedhyansow	-42443
+▁dheghowbarth	-42444
+▁dhinasydhyon	-42445
+▁dhioksiribon	-42446
+▁dhiskleryans	-42447
+▁dhiwysyansel	-42448
+▁dhornskrifow	-42449
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+▁dhyneydhyans	-42451
+▁didhregynnus	-42452
+▁difeythiryow	-42453
+▁dinythiansek	-42454
+▁disgyddiaeth	-42455
+▁dispossessed	-42456
+▁distillation	-42457
+▁divootteryow	-42458
+▁diwyethogeth	-42459
+▁domhwelansow	-42460
+▁dorfysegieth	-42461
+▁downsidhlans	-42462
+▁dowrdredanek	-42463
+▁druskininkai	-42464
+▁dustunyoryon	-42465
+▁démocratique	-42466
+▁ecolegiethek	-42467
+▁effeythuster	-42468
+▁ekologydhyon	-42469
+▁elephantidae	-42470
+▁erbysiethydh	-42471
+▁esplegyansel	-42472
+▁expectations	-42473
+▁fisiologieth	-42474
+▁fototredanek	-42475
+▁frankenstein	-42476
+▁frynkegoryon	-42477
+▁fydhyadoryon	-42478
+▁ganasedhores	-42479
+▁gemmynegorek	-42480
+▁genedlaethol	-42481
+▁geographical	-42482
+▁gernewegores	-42483
+▁gnasowarctek	-42484
+▁gordhownnhes	-42485
+▁gorhemmynans	-42486
+▁gorhwithrans	-42487
+▁gradhesigyon	-42488
+▁grestenogyon	-42489
+▁gwastatiryow	-42490
+▁gweythvannow	-42491
+▁gwinbrennyer	-42492
+▁gwreydhgefow	-42493
+▁gwrythyansow	-42494
+▁haberdashers	-42495
+▁harzquerbahn	-42496
+▁heksafluorid	-42497
+▁hentrevogyon	-42498
+▁howldreheval	-42499
+▁howlwriansow	-42500
+▁invertebrate	-42501
+▁iskowethyans	-42502
+▁jacksonville	-42503
+▁jungfraubahn	-42504
+▁kanasedhyans	-42505
+▁kargostentin	-42506
+▁katholigieth	-42507
+▁kehavalennow	-42508
+▁kellestekmur	-42509
+▁kellysgolyow	-42510
+▁kemmyniethek	-42511
+▁kemmyskansow	-42512
+▁kenedhlogydh	-42513
+▁kenedlegieth	-42514
+▁kesgovernans	-42515
+▁keshevelyans	-42516
+▁keskussulyer	-42517
+▁kespennsevik	-42518
+▁kesvargynnya	-42519
+▁kevarwodhyas	-42520
+▁killingworth	-42521
+▁kilwagrennow	-42522
+▁konsumyoryon	-42523
+▁korrvewonydh	-42524
+▁kupfernickel	-42525
+▁letherennegy	-42526
+▁leuvvedhegel	-42527
+▁lichtenstein	-42528
+▁longyearbyen	-42529
+▁lowarthoryon	-42530
+▁lughwriansek	-42531
+▁lyskannaseth	-42532
+▁martinsville	-42533
+▁mathematigal	-42534
+▁maximowiczii	-42535
+▁medhegnethel	-42536
+▁mengleudhyer	-42537
+▁mengleudhyor	-42538
+▁middendorffi	-42539
+▁mikhailovich	-42540
+▁miseducation	-42541
+▁molekulennow	-42542
+▁mongleudhyow	-42543
+▁municipality	-42544
+▁myghternesow	-42545
+▁myrmecophaga	-42546
+▁myskreydhyow	-42547
+▁nematomorpha	-42548
+▁newrologieth	-42549
+▁nightingales	-42550
+▁novemcinctus	-42551
+▁nowydhgwella	-42552
+▁omdhiberthis	-42553
+▁onderstondya	-42554
+▁orthotydhyon	-42555
+▁oskorblennye	-42556
+▁paleoargheek	-42557
+▁palestinians	-42558
+▁pellweloryow	-42559
+▁penneglosyow	-42560
+▁pennsylvanik	-42561
+▁pergherinyow	-42562
+▁pergherynses	-42563
+▁peterborough	-42564
+▁philippensis	-42565
+▁phytophthora	-42566
+▁plasennskrif	-42567
+▁plattdüütsch	-42568
+▁ponskesellte	-42569
+▁preferyansow	-42570
+▁pressgwiasva	-42571
+▁prestuplenie	-42572
+▁priweythvaow	-42573
+▁protestantek	-42574
+▁protobalanus	-42575
+▁rastafarieth	-42576
+▁renfrewshire	-42577
+▁repoblekanek	-42578
+▁rewlivonieth	-42579
+▁rewlivydhyon	-42580
+▁rosowbyldyes	-42581
+▁samudragupta	-42582
+▁saskatchewan	-42583
+▁selketalbahn	-42584
+▁shostakovich	-42585
+▁silmarillion	-42586
+▁siphonaptera	-42587
+▁skeusenweyth	-42588
+▁skoodhyansek	-42589
+▁skravinansow	-42590
+▁sosyalydhyon	-42591
+▁sosyologieth	-42592
+▁stadoberthys	-42593
+▁steanchikovo	-42594
+▁sterfysegydh	-42595
+▁tetradactyla	-42596
+▁thessalonika	-42597
+▁thoroughfare	-42598
+▁timberscombe	-42599
+▁translations	-42600
+▁treusenedhel	-42601
+▁treuslestrys	-42602
+▁treveglosyow	-42603
+▁trevesigetha	-42604
+▁trichechidae	-42605
+▁unverheansow	-42606
+▁vengleudhyow	-42607
+▁vezhinaerien	-42608
+▁warwickshire	-42609
+▁westinghouse	-42610
+▁wodhonydhyon	-42611
+▁worthtreylys	-42612
+▁оскорбленные	-42613
+▁степанчиково	-42614
+arrondissement	-42615
+glyptodontinae	-42616
+interprétation	-42617
+kowrbedresyfes	-42618
+meelelahutajad	-42619
+▁arbennikheans	-42620
+▁arhwithransow	-42621
+▁bengorfonieth	-42622
+▁bennfentennow	-42623
+▁berghennogeth	-42624
+▁bewdhoronieth	-42625
+▁borthdhewnens	-42626
+▁choloepodidae	-42627
+▁chrysanthemum	-42628
+▁citaarenebedh	-42629
+▁clannaborough	-42630
+▁congressional	-42631
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+▁contoyrtyssyn	-42633
+▁cyanobakteria	-42634
+▁dasfurvyansow	-42635
+▁derivasegieth	-42636
+▁deuterostomes	-42637
+▁devnydhyoryon	-42638
+▁dhiwreydhyans	-42639
+▁dhornskrifans	-42640
+▁difeythdiryow	-42641
+▁direwlegoryon	-42642
+▁diskudhyansow	-42643
+▁diwelvenogyon	-42644
+▁dreusfurvyans	-42645
+▁drevesigethow	-42646
+▁entertainment	-42647
+▁flemekdedhyas	-42648
+▁galwesigethow	-42649
+▁geskussulyans	-42650
+▁grestennogyon	-42651
+▁gwastastiryow	-42652
+▁gwenenydhieth	-42653
+▁gwreydhkyfyon	-42654
+▁gwreydhkyfyow	-42655
+▁hanterkelghow	-42656
+▁hanterkylghow	-42657
+▁hembrenkyades	-42658
+▁hembronkoryon	-42659
+▁hendhyscansek	-42660
+▁heseltineesel	-42661
+▁hesperidiomys	-42662
+▁hidrokarbonow	-42663
+▁holsteinsborg	-42664
+▁honanvewskrif	-42665
+▁ichthyosaures	-42666
+▁inquisiciones	-42667
+▁internacional	-42668
+▁ispoyntegieth	-42669
+▁jynnweythorek	-42670
+▁kammgemeryans	-42671
+▁kernewekheans	-42672
+▁kernowegoryon	-42673
+▁keweronydhyon	-42674
+▁keynansdiryow	-42675
+▁kigdhebroryon	-42676
+▁kostentinopel	-42677
+▁kovskrifennow	-42678
+▁kuntellyansow	-42679
+▁magapelecanus	-42680
+▁metamorphosis	-42681
+▁middlesbrough	-42682
+▁molybdophanes	-42683
+▁niedersachsen	-42684
+▁orthoptydhyon	-42685
+▁paynladhoryon	-42686
+▁pednsernethel	-42687
+▁pembrokeshire	-42688
+▁pennventynyow	-42689
+▁performyoryon	-42690
+▁phoeniconaias	-42691
+▁porthdhewnens	-42692
+▁privatisation	-42693
+▁priweythoryon	-42694
+▁prononciation	-42695
+▁reminiscences	-42696
+▁representyans	-42697
+▁septemcinctus	-42698
+▁splannvraster	-42699
+▁stepanchikovo	-42700
+▁sterdhewinydh	-42701
+▁stuartcharlys	-42702
+▁tanbellenigow	-42703
+▁trevesikheans	-42704
+▁trihornegyans	-42705
+▁tubulidentata	-42706
+▁universitaire	-42707
+▁unnchambourek	-42708
+▁unyansegoryon	-42709
+▁valentinianus	-42710
+▁volusiamayour	-42711
+▁wiedringharde	-42712
+▁withadoregyon	-42713
+▁zooxanthellae	-42714
+▁zoulouegoryon	-42715
+▁петербургская	-42716
+treusfurvyansow	-42717
+▁acanthocephala	-42718
+▁afrikabirdlife	-42719
+▁aguascalientes	-42720
+▁arabekpenncita	-42721
+▁arbeiterpartei	-42722
+▁bavariaranndir	-42723
+▁chlamyphoridae	-42724
+▁constellations	-42725
+▁czechoslovakia	-42726
+▁dastineythyans	-42727
+▁degvledhynnyow	-42728
+▁dengerenjedhek	-42729
+▁dhevnydhyoryon	-42730
+▁dhifeythtiryow	-42731
+▁dhirewlegoryon	-42732
+▁erghpednaghlek	-42733
+▁etymologydhyon	-42734
+▁europekchamber	-42735
+▁floridakonteth	-42736
+▁gordhuherweyth	-42737
+▁gwelsdebroryon	-42738
+▁hanterkendegor	-42739
+▁internationale	-42740
+▁kanatsiohareke	-42741
+▁kenedlegiethek	-42742
+▁kesstrivyansow	-42743
+▁komprehendyans	-42744
+▁lagenorhynchus	-42745
+▁liesdhuwiethek	-42746
+▁longipetiolata	-42747
+▁meteorologieth	-42748
+▁milvilclasyans	-42749
+▁narasimhagupta	-42750
+▁nordaustlandet	-42751
+▁ombortrayansow	-42752
+▁ostraliaustral	-42753
+▁parasitiformes	-42754
+▁pennseythenyow	-42755
+▁prescriptivism	-42756
+▁prosthetydhyon	-42757
+▁psychoanalysis	-42758
+▁soroastrianeth	-42759
+▁treussplanadow	-42760
+▁udnformscrefys	-42761
+▁ughkendegoryon	-42762
+▁västernorrland	-42763
+▁weriniethoryon	-42764
+▁ynbyldyorniver	-42765
+sociolinguistics	-42766
+▁bysyelwordhonek	-42767
+▁calyptophractus	-42768
+▁cyclobalanopsis	-42769
+▁dhiskwedhyansow	-42770
+▁disillusionment	-42771
+▁diwysyansegyans	-42772
+▁francoprovençal	-42773
+▁gnathostomulida	-42774
+▁gorthugherweyth	-42775
+▁gorwodhoniethek	-42776
+▁hendhyskonydhes	-42777
+▁huntingdonshire	-42778
+▁istoriografieth	-42779
+▁kansvledhydnyow	-42780
+▁karthgleudhyans	-42781
+▁mathemategydhes	-42782
+▁myrmecophagidae	-42783
+▁nottinghamshire	-42784
+▁pennsilfaanisch	-42785
+▁platyhelminthes	-42786
+▁representatives	-42787
+▁spektroskopieth	-42788
+▁tenthredinoidea	-42789
+▁ughelgowrsteren	-42790
+▁vrysoniethoryon	-42791
+,)>	-42792
+dɛg	-42793
+kæm	-42794
+kɔf	-42795
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+nác	-42800
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+pæl	-42802
+pìz	-42803
+síd	-42804
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+tìn	-42806
+vɛj	-42807
+wmb	-42808
+zīd	-42809
+ærɑ	-42810
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+υσόσ	-42927
+χθυσ	-42928
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+дний	-42931
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+َيْن	-42956
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+▁bgc	-42960
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+▁bʁɔ	-42965
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+▁ihc	-42968
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+▁або	-42988
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+▁буд	-42990
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+▁ابن	-42995
+▁بَط	-42996
+▁شيع	-42997
+▁عيس	-42998
+▁عَك	-42999
+gyda	-43000
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+▁كبس	-43008
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+beɪdn	-43020
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+floor	-43049
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+hɛʁta	-43060
+ieuan	-43061
+iezer	-43062
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+▁مريم	-49040
+▁ἐλθέ	-49041
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+▁teays	-49052
+▁τεύτα	-49053
+▁الدين	-49054
+▁بعجين	-49055
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+goelyow	-49058
+ləvjɪtɕ	-49059
+ètebait	-49060
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+▁ashbya	-49063
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+▁robbya	-49065
+▁seneca	-49066
+▁trobya	-49067
+▁zarzón	-49068
+▁ведьмы	-49069
+▁الكيلة	-49070
+▁ἐλθέτω	-49071
+arwennak	-49072
+diachron	-49073
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+viliškis	-49075
+vorennoù	-49076
+xcalteca	-49077
+οποταμία	-49078
+складний	-49079
+▁ardiaei	-49080
+▁cathays	-49081
+▁chineca	-49082
+▁ratujmy	-49083
+▁σήμερον	-49084
+▁العامري	-49085
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+ajləvjɪtɕ	-49087
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+bɛl	-49102
+hmk	-49103
+nɛl	-49104
+újv	-49105
+āhā	-49106
+ʃɛl	-49107
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+bröö	-49109
+būtī	-49110
+bɛl̪	-49111
+loyi	-49112
+meîs	-49113
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+yyât	-49116
+újvá	-49117
+▁ihm	-49118
+▁tdb	-49119
+▁töö	-49120
+▁vės	-49121
+▁wɛl	-49122
+▁áyi	-49123
+anoîs	-49124
+brööð	-49125
+bɛl̪ɣ	-49126
+hloyi	-49127
+nɛliz	-49128
+ībūtī	-49129
+▁brux	-49130
+▁brîs	-49131
+▁brāh	-49132
+▁enrí	-49133
+...'?"	-49134
+agríms	-49135
+edhacw	-49136
+riyyât	-49137
+▁dizzy	-49138
+▁dreux	-49139
+▁ilāhā	-49140
+▁tööle	-49141
+▁vėsūs	-49142
+aliarct	-49143
+erbrööð	-49144
+újváros	-49145
+▁hêmeîs	-49146
+▁shingi	-49147
+▁throws	-49148
+▁áyiila	-49149
+sgotlond	-49150
+unihloyi	-49151
+ðerbrööð	-49152
+▁algieba	-49153
+agrímsson	-49154
+edhacwymp	-49155
+elementos	-49156
+emonoides	-49157
+enaliarct	-49158
+eneration	-49159
+enntnisse	-49160
+entialist	-49161
+erithacus	-49162
+ernpartei	-49163
+erotrites	-49164
+esperanto	-49165
+esplegyas	-49166
+esslynkow	-49167
+estemyans	-49168
+esterbled	-49169
+etbegović	-49170
+ethyresek	-49171
+etiquette	-49172
+euphrates	-49173
+evansvill	-49174
+evbraster	-49175
+eyndogyon	-49176
+fairbanks	-49177
+fastleigh	-49178
+faveryans	-49179
+feinstein	-49180
+fflintown	-49181
+finitivos	-49182
+finwethek	-49183
+fortsmith	-49184
+foundland	-49185
+frankonek	-49186
+frontalis	-49187
+fûgelfrij	-49188
+galesburg	-49189
+gammbronn	-49190
+gazetteer	-49191
+gigameter	-49192
+glanheans	-49193
+glichkeit	-49194
+gramineae	-49195
+grammatic	-49196
+greenwich	-49197
+gwandrans	-49198
+gweusgweu	-49199
+cripts	-49200
+emutum	-49201
+▁menhad	-49202
+▁pampas	-49203
+▁kesseni	-49204
+▁mysteri	-49205
+▁therapi	-49206
+▁tonsbal	-49207
+catherine	-49208
+gwithtiow	-49209
+hamriyyât	-49210
+hankewsys	-49211
+hanugupta	-49212
+havencity	-49213
+heartbeat	-49214
+hecimento	-49215
+henwyskan	-49216
+hillsboro	-49217
+hippopota	-49218
+hwarheans	-49219
+hwetegves	-49220
+iacetylen	-49221
+iclopedia	-49222
+idaysburg	-49223
+ielczarek	-49224
+igotrofow	-49225
+igungsges	-49226
+ikotropin	-49227
+ikrayatun	-49228
+ilietuvos	-49229
+inerarium	-49230
+inessiivi	-49231
+irregular	-49232
+isasyonow	-49233
+johnstown	-49234
+josephine	-49235
+jɪrəvjɪtɕ	-49236
+kelesonek	-49237
+kembroyon	-49238
+kemeryans	-49239
+kenwertha	-49240
+kesskorys	-49241
+kevrinyow	-49242
+kiswahili	-49243
+kledhrenn	-49244
+kontiliow	-49245
+kontralto	-49246
+korevigow	-49247
+kowethyes	-49248
+lagwariys	-49249
+laskorras	-49250
+laväljade	-49251
+leability	-49252
+lesiastes	-49253
+lewisburg	-49254
+linicombe	-49255
+longbeach	-49256
+losowieth	-49257
+lostledan	-49258
+loxodonta	-49259
+magdalena	-49260
+magnesiom	-49261
+makologyl	-49262
+malayalam	-49263
+mantisyow	-49264
+marctinae	-49265
+mariehamn	-49266
+maulipeco	-49267
+mauveseag	-49268
+mcinnerny	-49269
+megameter	-49270
+meirchion	-49271
+meiriadog	-49272
+mellyonen	-49273
+menydhyek	-49274
+messenger	-49275
+meuledhek	-49276
+milwaukee	-49277
+mitivieth	-49278
+modernism	-49279
+monuments	-49280
+morgowles	-49281
+mperdinck	-49282
+munegascu	-49283
+muttahida	-49284
+nashville	-49285
+newmexico	-49286
+nonovaria	-49287
+oaxaqueño	-49288
+ocornovio	-49289
+odiopsida	-49290
+oflifesun	-49291
+oforlando	-49292
+ogevreyth	-49293
+ognathous	-49294
+oiltaghey	-49295
+okaderyer	-49296
+okevreyth	-49297
+okouezhañ	-49298
+oksiribos	-49299
+dždž	-49300
+ysês	-49301
+arbus	-49302
+gabbro	-49303
+▁nimoy	-49304
+▁striw	-49305
+obarbus	-49306
+▁buffon	-49307
+▁dulais	-49308
+▁feusik	-49309
+▁môysês	-49310
+▁sleepy	-49311
+▁strigi	-49312
+▁tolstó	-49313
+▁trusty	-49314
+chblende	-49315
+looideae	-49316
+ofelidae	-49317
+ographic	-49318
+uculidae	-49319
+uvyanjow	-49320
+▁bewdekn	-49321
+▁honnaow	-49322
+▁hystrix	-49323
+▁llansad	-49324
+▁melinji	-49325
+▁neverbe	-49326
+▁sebasti	-49327
+▁shepard	-49328
+▁stirner	-49329
+▁strider	-49330
+▁striped	-49331
+▁tolstóy	-49332
+annapolis	-49333
+bliografi	-49334
+edhyanjow	-49335
+idealieth	-49336
+ographiae	-49337
+oksitanek	-49338
+okópoulos	-49339
+olesterol	-49340
+olglamour	-49341
+ollgemmyn	-49342
+ollinaire	-49343
+ombrofyer	-49344
+omdednans	-49345
+omguntell	-49346
+omiedhyon	-49347
+onometrek	-49348
+ontroulez	-49349
+onychidae	-49350
+opagitica	-49351
+ophoridae	-49352
+ophthalma	-49353
+oramorpha	-49354
+ordinated	-49355
+orhynchus	-49356
+osardinia	-49357
+ospelikan	-49358
+osphenida	-49359
+othyronin	-49360
+oùgóllbou	-49361
+partisyrr	-49362
+pensacola	-49363
+peswarpaw	-49364
+petameter	-49365
+peterbúrg	-49366
+philosoph	-49367
+plastogow	-49368
+plethadow	-49369
+ploudalmé	-49370
+podravska	-49371
+politeger	-49372
+pomeranek	-49373
+ponsfordh	-49374
+português	-49375
+promovyes	-49376
+protolith	-49377
+proviades	-49378
+providenc	-49379
+pwllgwyng	-49380
+qissariya	-49381
+quartzite	-49382
+rakatulûk	-49383
+ransitive	-49384
+ranstonri	-49385
+rassidens	-49386
+readingpa	-49387
+resenieth	-49388
+ricksgwag	-49389
+ridgeport	-49390
+rightraph	-49391
+robertson	-49392
+ropoyetin	-49393
+rotheriid	-49394
+rovaniemi	-49395
+roversita	-49396
+rylloides	-49397
+samphibia	-49398
+screaming	-49399
+▁вис	-49400
+▁ста́	-49401
+kristy	-49402
+tressa	-49403
+▁costs	-49404
+▁gorrv	-49405
+▁mongu	-49406
+ostrata	-49407
+stivell	-49408
+unswick	-49409
+▁abelii	-49410
+▁chelms	-49411
+▁gormol	-49412
+▁gudwal	-49413
+▁lennir	-49414
+▁mongow	-49415
+▁morwuw	-49416
+▁nammys	-49417
+▁scolop	-49418
+▁vostra	-49419
+▁ста́ли	-49420
+kristina	-49421
+muritani	-49422
+rumental	-49423
+urnmaine	-49424
+▁abelard	-49425
+▁asleans	-49426
+▁chillon	-49427
+▁cochran	-49428
+▁dioklet	-49429
+▁espaina	-49430
+▁eylvyld	-49431
+▁gormola	-49432
+▁hockney	-49433
+▁humaine	-49434
+▁mansion	-49435
+▁messent	-49436
+▁messick	-49437
+▁messyas	-49438
+▁mollard	-49439
+▁mongoos	-49440
+▁mongraf	-49441
+▁monguor	-49442
+▁mooring	-49443
+▁plomber	-49444
+▁sancell	-49445
+▁sanctae	-49446
+▁виссари	-49447
+▁ста́лин	-49448
+brunswick	-49449
+conceived	-49450
+seinajoki	-49451
+seytegves	-49452
+shalakany	-49453
+siplegyas	-49454
+skovarnek	-49455
+skwattyas	-49456
+slovensko	-49457
+sohoplace	-49458
+somewhere	-49459
+sonaekwil	-49460
+southaven	-49461
+southbend	-49462
+sozialist	-49463
+srijemska	-49464
+stadtroda	-49465
+steindach	-49466
+stondyans	-49467
+stopwatch	-49468
+stsvenska	-49469
+sundsvall	-49470
+supercond	-49471
+syskynnas	-49472
+telleryow	-49473
+tenodytes	-49474
+terameter	-49475
+teriology	-49476
+thenosfer	-49477
+thyroydek	-49478
+tificetur	-49479
+tomatieth	-49480
+trentonnj	-49481
+trihornek	-49482
+trovannel	-49483
+uardiente	-49484
+ugolethow	-49485
+ulayṭulah	-49486
+uncularis	-49487
+uspieldir	-49488
+valeriana	-49489
+veisbergs	-49490
+vektoryow	-49491
+venanmaan	-49492
+venenrydh	-49493
+versation	-49494
+viricetes	-49495
+visaginas	-49496
+vulcanize	-49497
+wchhaearn	-49498
+welleryow	-49499
+сский	-49500
+▁kidn	-49501
+▁majd	-49502
+▁suky	-49503
+▁sygd	-49504
+albany	-49505
+ffotys	-49506
+jersey	-49507
+kaunas	-49508
+lenska	-49509
+teurek	-49510
+tierra	-49511
+ynthos	-49512
+▁andhy	-49513
+▁cefin	-49514
+▁choir	-49515
+▁cured	-49516
+▁fouke	-49517
+▁hosea	-49518
+▁hosni	-49519
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+▁lumet	-49521
+▁mohri	-49522
+▁mulap	-49523
+▁negos	-49524
+▁sveam	-49525
+▁sygod	-49526
+▁tepan	-49527
+▁tepic	-49528
+▁tepui	-49529
+quettes	-49530
+русский	-49531
+▁choano	-49532
+▁dimitt	-49533
+▁hafnon	-49534
+▁heglow	-49535
+▁jarrik	-49536
+▁javier	-49537
+▁kofhes	-49538
+▁majora	-49539
+▁mulino	-49540
+▁muller	-49541
+▁mutant	-49542
+▁oorliv	-49543
+▁sacrif	-49544
+▁yuriko	-49545
+lovenska	-49546
+▁ancrene	-49547
+▁dimitte	-49548
+▁gwilkyn	-49549
+▁javanek	-49550
+▁kanzler	-49551
+▁kidnapp	-49552
+▁kidnyav	-49553
+▁kidston	-49554
+▁mulapin	-49555
+▁negosas	-49556
+▁rudolph	-49557
+▁sukyand	-49558
+▁sveamål	-49559
+▁sygdomm	-49560
+▁tepanec	-49561
+▁tierney	-49562
+korrgarow	-49563
+vonlenska	-49564
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+xgrenoble	-49566
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+ydhyansel	-49568
+ykarbonat	-49569
+ylobacter	-49570
+ymplectic	-49571
+ynystieth	-49572
+yonkersny	-49573
+yotameter	-49574
+yperoplus	-49575
+ysqwedhes	-49576
+zbekiston	-49577
+zeppelins	-49578
+zetameter	-49579
+ziukalnas	-49580
+zliplèvre	-49581
+ñvorennoù	-49582
+ɛmmɛlvɛjs	-49583
+ɡjejɪvjɪt	-49584
+ɪstʃənsən	-49585
+εοτοκόπου	-49586
+λλιγραφία	-49587
+φειλέταισ	-49588
+арковский	-49589
+атольевич	-49590
+доконаний	-49591
+маленький	-49592
+петербург	-49593
+пива́сики	-49594
+подросток	-49595
+униженные	-49596
+عبّاسيّون	-49597
+▁abercarn	-49598
+▁aberdour	-49599
+hansa	-49600
+ligue	-49601
+pisko	-49602
+wrach	-49603
+aroles	-49604
+chonte	-49605
+flicks	-49606
+gwrach	-49607
+langev	-49608
+mithun	-49609
+mustar	-49610
+pposys	-49611
+rolled	-49612
+rollya	-49613
+tamara	-49614
+▁irida	-49615
+▁kicks	-49616
+hardlan	-49617
+langoes	-49618
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+▁alicia	-49622
+▁ecplor	-49623
+▁erhard	-49624
+▁erlang	-49625
+▁luhans	-49626
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+langevni	-49635
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+pedncita	-49638
+pednfent	-49639
+platinom	-49640
+rangifer	-49641
+supposys	-49642
+▁allsens	-49643
+▁caperea	-49644
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+▁diwlenn	-49646
+▁gorhebm	-49647
+▁marmara	-49648
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+killarney	-49651
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+▁acoustek	-49657
+▁acoustic	-49658
+▁adenewan	-49659
+▁adenosin	-49660
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+▁aegagrus	-49662
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+▁afrobask	-49664
+▁afydhyas	-49665
+▁agglutin	-49666
+▁alderney	-49667
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+▁alleanza	-49669
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+▁allmusic	-49671
+▁alphonse	-49672
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+▁aluminid	-49674
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+▁amazonsp	-49676
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+▁ancaster	-49680
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+▁anchovia	-49682
+▁andalusi	-49683
+▁anerkann	-49684
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+▁anfurvel	-49686
+▁anhornek	-49687
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+▁ankoryow	-49689
+▁annelids	-49690
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+▁köt	-49700
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+▁ibup	-49708
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+▁aepyceros	-54996
+▁africanum	-54997
+▁alamandow	-54998
+▁alawityon	-54999
+)</	-55000
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+▁sonerezh	-55087
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+▁allentown	-55092
+▁almodóvar	-55093
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+▁alternata	-55095
+▁aluseneth	-55096
+▁amauensis	-55097
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+▁βο	-55102
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+▁фёд	-55110
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+▁hahn	-55112
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+▁gorky	-55117
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+▁фёдор	-55121
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+▁anggun	-55123
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+▁kruger	-55126
+▁niacin	-55127
+▁prysky	-55128
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+▁selenid	-55140
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+▁emocynal	-55149
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+▁amyttyans	-55161
+▁anaboledh	-55162
+▁anayrobek	-55163
+▁anbarghus	-55164
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+▁anfetelek	-55168
+▁anfurvyel	-55169
+▁ankreshes	-55170
+▁ankresyes	-55171
+▁anlettrys	-55172
+▁annedhyas	-55173
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+▁antifonek	-55176
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+▁antiqwary	-55178
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+▁arbednick	-55184
+▁arbenigys	-55185
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+▁asmerthyr	-55198
+▁aspiansek	-55199
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+kahəsɣ	-55202
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+kahəsɣi	-55205
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+▁owenii	-55214
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+▁القزاز	-55218
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+▁kartism	-55229
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+▁mangrov	-55234
+▁marxist	-55235
+▁mustang	-55236
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+▁peskpan	-55238
+▁peurell	-55239
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+▁dhivronn	-55244
+▁duodenal	-55245
+▁garthyon	-55246
+▁grantown	-55247
+▁greenfly	-55248
+▁gwythres	-55249
+▁gwythyas	-55250
+▁kartulek	-55251
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+▁scorcher	-55253
+▁socyalek	-55254
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+▁tiskarga	-55256
+▁القزازين	-55257
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+▁astewisas	-55262
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+▁astudyans	-55264
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+▁asweythva	-55266
+▁athleteks	-55267
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+▁attitudes	-55269
+▁attoeylen	-55270
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+▁australes	-55272
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+▁awenekter	-55274
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+▁babelfish	-55276
+▁bacillari	-55277
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+▁bankennow	-55280
+▁baramanyn	-55281
+▁barlangjá	-55282
+▁barrednow	-55283
+▁bassetlaw	-55284
+▁bastellow	-55285
+▁bastnasit	-55286
+▁battalion	-55287
+▁battalyon	-55288
+▁battister	-55289
+▁bednaghel	-55290
+▁bednsevik	-55291
+▁beekeeper	-55292
+▁beinteìns	-55293
+▁benbecula	-55294
+▁beneleger	-55295
+▁benelegor	-55296
+▁bennskoll	-55297
+▁bentgrass	-55298
+▁bernaklys	-55299
+▁еж	-55300
+yamē	-55301
+▁srá	-55302
+pingo	-55303
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+▁bags	-55305
+▁dagg	-55306
+▁depo	-55307
+▁hspa	-55308
+▁røde	-55309
+▁wasg	-55310
+▁wasn	-55311
+alláin	-55312
+intyre	-55313
+ipinas	-55314
+mytern	-55315
+pionus	-55316
+quysys	-55317
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+yamēva	-55319
+ygerir	-55320
+▁bingo	-55321
+▁bohod	-55322
+▁cossi	-55323
+▁girgh	-55324
+▁girob	-55325
+▁jingo	-55326
+▁nemea	-55327
+▁nemec	-55328
+▁races	-55329
+▁rajoy	-55330
+▁ringo	-55331
+▁sráid	-55332
+▁tetum	-55333
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+kolorys	-55339
+polygen	-55340
+▁baglan	-55341
+▁bohren	-55342
+▁kubuli	-55343
+▁nemitz	-55344
+▁nemmys	-55345
+▁nemuru	-55346
+▁racond	-55347
+▁sitter	-55348
+▁tetram	-55349
+▁unwisk	-55350
+▁viston	-55351
+ignature	-55352
+wrysegys	-55353
+▁aniston	-55354
+▁bohodga	-55355
+▁bradsha	-55356
+▁cossiga	-55357
+▁devysys	-55358
+▁dhiveth	-55359
+▁droppya	-55360
+▁elísson	-55361
+▁frappya	-55362
+▁mozilla	-55363
+▁nemestr	-55364
+▁raconda	-55365
+▁wasgedd	-55366
+errerense	-55367
+quysysyon	-55368
+signature	-55369
+▁censorys	-55370
+▁coniston	-55371
+▁corbalen	-55372
+▁flamingo	-55373
+▁girobank	-55374
+▁mamoleth	-55375
+▁mcintyre	-55376
+▁mealláin	-55377
+▁perniger	-55378
+▁unwiskow	-55379
+▁viscount	-55380
+▁bertholau	-55381
+▁bewteknek	-55382
+▁bimbochet	-55383
+▁bisemutum	-55384
+▁biswakobi	-55385
+▁blakeslee	-55386
+▁blanettek	-55387
+▁blankenes	-55388
+▁bleynamal	-55389
+▁bloomberg	-55390
+▁bloutonek	-55391
+▁bluvennow	-55392
+▁bodffordd	-55393
+▁bohodgack	-55394
+▁bohurrowe	-55395
+▁bohurthow	-55396
+▁bolivarek	-55397
+▁bolshevyk	-55398
+▁bolymerow	-55399
+juli	-55400
+preš	-55401
+▁eih	-55402
+▁ipc	-55403
+▁zoë	-55404
+antai	-55405
+bouli	-55406
+julij	-55407
+musch	-55408
+phyla	-55409
+tilia	-55410
+tunia	-55411
+tunic	-55412
+yacek	-55413
+étude	-55414
+▁gyla	-55415
+▁yuli	-55416
+acanth	-55417
+annuli	-55418
+antais	-55419
+eshoua	-55420
+friuli	-55421
+hailck	-55422
+ifolys	-55423
+igeach	-55424
+phanth	-55425
+preder	-55426
+prešov	-55427
+satnas	-55428
+tunisi	-55429
+uranth	-55430
+▁baiji	-55431
+▁beppo	-55432
+▁motil	-55433
+▁peach	-55434
+▁skyla	-55435
+▁teyla	-55436
+▁twyni	-55437
+▁ulmus	-55438
+asidium	-55439
+ghtside	-55440
+igenori	-55441
+iodinas	-55442
+iseanta	-55443
+lebeach	-55444
+rhyacek	-55445
+uliidae	-55446
+▁fertil	-55447
+▁fityes	-55448
+▁friuli	-55449
+▁granta	-55450
+▁janick	-55451
+▁kelmus	-55452
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+eachment	-55454
+julijska	-55455
+nebraska	-55456
+prederys	-55457
+sligeach	-55458
+àiseanta	-55459
+▁bulrush	-55460
+▁ghailck	-55461
+▁glastow	-55462
+▁kawghys	-55463
+▁motilal	-55464
+▁nantais	-55465
+▁yeshoua	-55466
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+phanthiet	-55468
+▁atalanta	-55469
+▁baldrick	-55470
+▁beijerin	-55471
+▁daromles	-55472
+▁dhifolys	-55473
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+▁keunegen	-55477
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+▁brassicae	-55489
+▁brauchitz	-55490
+▁brednygen	-55491
+▁bregothas	-55492
+▁brentwood	-55493
+▁bressuire	-55494
+▁bretonyon	-55495
+▁breuddwyd	-55496
+▁breviceps	-55497
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+▁brifysgol	-55499
+énm	-55500
+éné	-55501
+▁зз	-55502
+▁ох	-55503
+▁чч	-55504
+▁чш	-55505
+demm	-55506
+fosi	-55507
+glén	-55508
+josé	-55509
+kolc	-55510
+kolk	-55511
+kolm	-55512
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